Propositional and Relational Bayesian Networks Associated with Imprecise and Qualitative Probabilistic Assessments

This paper investigates a representation language with flexibility inspired by probabilistic logic and compactness inspired by relational Bayesian networks. The goal is to handle propositional and first-order constructs together with precise, imprecise, indeterminate and qualitative probabilistic assessments. The paper shows how this can be achieved through the theory of credal networks. New exact and approximate inference algorithms based on multilinear programming and iterated/loopy propagation of interval probabilities are presented; their superior performance, compared to existing ones, is shown empirically.

Belief Updating and Learning in Semi-Qualitative Probabilistic Networks

This paper explores semi-qualitative probabilistic networks (SQPNs) that combine numeric and qualitative information. We first show that exact inferences with SQPNs are NPPP-Complete. We then show that existing qualitative relations in SQPNs (plus probabilistic logic and imprecise assessments) can be dealt effectively through multilinear programming. We then discuss learning: we consider a maximum likelihood method that generates point estimates given a SQPN and empirical data, and we describe a Bayesian-minded method that employs the Imprecise Dirichlet Model to generate set-valued estimates.

Overview of the current ISO tests for photocatalytic materials

The current eight published ISO standards associated with semiconductor photocatalysis are considered. These standards cover: (1) air purification (specifically, the removal of NO, acetaldehyde and toluene), (2) water purification (the photobleaching of methylene blue and oxidation of DMSO) (3) self-cleaning surfaces (the removal of oleic acid and subsequent change in water droplet contact angle), (4) photosterilisation (specifically probing the antibacterial action of semiconductor photocatalyst films) and (5) UV light sources for semiconductor photocatalytic ISO work. For each standard, the background is first considered, followed by a brief discussion of the standard particulars and concluding in a discussion of the pros and cons of the standard, with often recommendations for their improvement. Other possible standards for the future which would either compliment or enhance the current ones are discussed briefly. 

Plan Selection for Probabilistic BDI Agents

When an agent wants to fulfill its desires about the world, the agent usually has multiple plans to choose from and these plans have different pre-conditions and additional effects in addition to achieving its goals. Therefore, for further reasoning and interaction with the world, a plan selection strategy (usually based on plan cost estimation) is mandatory for an autonomous agent. This demand becomes even more critical when uncertainty on the observation of the world is taken into account, since in this case, we consider not only the costs of different plans, but also their chances of success estimated according to the agent's beliefs. In addition, when multiple goals are considered together, different plans achieving the goals can be conflicting on their preconditions (contexts) or the required resources. Hence a plan selection strategy should be able to choose a subset of plans that fulfills the maximum number of goals while maintaining context consistency and resource-tolerance among the chosen plans. To address the above two issues, in this paper we first propose several principles that a plan selection strategy should satisfy, and then we present selection strategies that stem from the principles, depending on whether a plan cost is taken into account. In addition, we also show that our selection strategy can partially recover intention revision.

Hybrid probabilistic wind power forecasting using temporally local Gaussian process

The demand for sustainable development has resulted in a rapid growth in wind power worldwide. Despite various approaches have been proposed to improve the accuracy and to overcome the uncertainties associated with traditional methods, the stochastic and variable nature of wind still remains the most challenging issue in accurately forecasting wind power. This paper presents a hybrid deterministic-probabilistic method where a temporally local ‘moving window’ technique is used in Gaussian Process to examine estimated forecasting errors. This temporally local Gaussian Process employs less measurement data while faster and better predicts wind power at two wind farms, one in the USA and the other in Ireland. Statistical analysis on the results shows that the method can substantially reduce the forecasting error while more likely generate Gaussian-distributed residuals, particularly for short-term forecast horizons due to its capability to handle the time-varying characteristics of wind power.

Game-­theoretic Resource Allocation with Real-­time Probabilistic Surveillance Information

Game-theoretic security resource allocation problems have generated significant interest in the area of designing and developing security systems. These approaches traditionally utilize the Stackelberg game model for security resource scheduling in order to improve the protection of critical assets. The basic assumption in Stackelberg games is that a defender will act first, then an attacker will choose their best response after observing the defender’s strategy commitment (e.g., protecting a specific asset). Thus, it requires an attacker’s full or partial observation of a defender’s strategy. This assumption is unrealistic in real-time threat recognition and prevention. In this paper, we propose a new solution concept (i.e., a method to predict how a game will be played) for deriving the defender’s optimal strategy based on the principle of acceptable costs of minimax regret. Moreover, we demonstrate the advantages of this solution concept by analyzing its properties.

The validity of critical thinking tests for predicting degree performance: A longitudinal study

This study explored the validity of using critical thinking tests to predict final psychology degree marks over and above that already predicted by traditional admission exams (A-levels). Participants were a longitudinal sample of 109 psychology students from a university in the United Kingdom. The outcome measures were: total degree marks; and end of year marks. The predictor measures were: university admission exam results (A-levels); critical thinking test scores (skills & dispositions); and non-verbal intelligence scores. Hierarchical regressions showed A-levels significantly predicted 10% of the final degree score and the 11-item measure of ‘Inference skills’ from the California Critical Thinking Skills Test significantly predicted an additional 6% of degree outcome variance. The findings from this study should inform decisions about the precise measurement constructs included in aptitude tests used in the higher education admission process.

Novel Panel Cointegration Tests Emending for Cross-Section Dependence with N Fixed

In this paper, we propose new cointegration tests for single equations and panels. Inboth cases, the asymptotic distributions of the tests, which are derived with N fixed andT → ∞, are shown to be standard normals. The effects of serial correlation and crosssectionaldependence are mopped out via long-run variances. An effective bias correctionis derived which is shown to work well in finite samples; particularly when N is smallerthan T. Our panel tests are robust to possible cointegration across units.